Somnotate: A probabilistic sleep stage classifier for studying vigilance state transitions.

Electrophysiological recordings from freely behaving animals are a widespread and powerful mode of investigation in sleep research. These recordings generate large amounts of data that require sleep stage annotation (polysomnography), in which the data is parcellated according to three vigilance states: awake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep. Manual and current computational annotation methods ignore intermediate states because the classification features become ambiguous, even though intermediate states contain important information regarding vigilance state dynamics. To address this problem, we have developed "Somnotate"-a probabilistic classifier based on a combination of linear discriminant analysis (LDA) with a hidden Markov model (HMM). First we demonstrate that Somnotate sets new standards in polysomnography, exhibiting annotation accuracies that exceed human experts on mouse electrophysiological data, remarkable robustness to errors in the training data, compatibility with different recording configurations, and an ability to maintain high accuracy during experimental interventions. However, the key feature of Somnotate is that it quantifies and reports the certainty of its annotations. We leverage this feature to reveal that many intermediate vigilance states cluster around state transitions, whereas others correspond to failed attempts to transition. This enables us to show for the first time that the success rates of different types of transition are differentially affected by experimental manipulations and can explain previously observed sleep patterns. Somnotate is open-source and has the potential to both facilitate the study of sleep stage transitions and offer new insights into the mechanisms underlying sleep-wake dynamics.

Neural signatures of sleep in zebrafish.

Slow-wave sleep and rapid eye movement (or paradoxical) sleep have been found in mammals, birds and lizards, but it is unclear whether these neuronal signatures are found in non-amniotic vertebrates. Here we develop non-invasive fluorescence-based polysomnography for zebrafish, and show-using unbiased, brain-wide activity recording coupled with assessment of eye movement, muscle dynamics and heart rate-that there are at least two major sleep signatures in zebrafish. These signatures, which we term slow bursting sleep and propagating wave sleep, share commonalities with those of slow-wave sleep and paradoxical or rapid eye movement sleep, respectively. Further, we find that melanin-concentrating hormone signalling (which is involved in mammalian sleep) also regulates propagating wave sleep signatures and the overall amount of sleep in zebrafish, probably via activation of ependymal cells. These observations suggest that common neural signatures of sleep may have emerged in the vertebrate brain over 450 million years ago.

Nocturnal hypoxemic burden in patients with heart failure: Emerging prognostic role of its nonspecific component.

BACKGROUND: Nocturnal hypoxemic burden has been shown to be a robust, independent predictor of all-cause mortality in patients with heart failure and reduced ejection fraction (HFrEF) and to occur in a severe form even in patients with low or negligible frequency of respiratory events (apneas/hypopneas). This suggests the existence of two components of hypoxemic burden: one unrelated to respiratory events and the other related. The aim of this study was to characterize these two components and to evaluate their prognostic value. METHODS: Nocturnal hypoxemic burden was assessed in a cohort of 280 patients with HFrEF by measuring the percentage of sleep with an oxygen saturation (SpO2) <90% (T90), and the area of the SpO2 curve below 90% (Area90). Both indices were also recalculated within the sleep segments associated with respiratory events (event-related component: T90Eve, Area90Eve) and outside these segments (nonspecific component: T90Nspec, Area90Nspec). The outcome of the survival analysis (Cox regression) was all-cause mortality. RESULTS: During a median follow-up of 60 months, 87 patients died. T90, Area90, and their components were significant in univariate analysis (P < .05 all). However, when these indices were adjusted for known risk factors, T90, T90Nspec, Area90, and Area90Nspec remained statistically significant (P = .018, hazard ratio (HR)=1.12, 95%CI=(1.02, 1.23); P = .007, HR=1.20, 95% CI = [1.05, 1.37]; P = .020, HR = 1.05, 95% CI = [1.01, 1.10]; P = .0006, HR = 1.15, 95% CI = [1.06, 1.25]), whereas T90Eve and Area90Eve did not (P = .27, P = .28). These results were internally validated using bootstrap resampling. CONCLUSIONS: By demonstrating a significant independent association of nonspecific hypoxemic burden with all-cause mortality, this study suggests that this component of total nocturnal hypoxemic burden may play an important prognostic role in patients with HFrEF.

Predictors of moderate to severe obstructive sleep apnea in patients with lung cancer.

BACKGROUND AND OBJECTIVES: OSA is a known medical condition that is associated with several comorbidities and affect patients' quality of life. The association between OSA and lung cancer remains debated. Some studies reported increased prevalence of OSA in patients with lung cancer. We aimed to assess predictors of moderate-to-severe OSA in patients with lung cancer. METHODS: We enrolled 153 adult patients who were newly diagnosed with lung cancer. Cardiorespiratory monitoring was performed using home sleep apnea device. We carried out Univariate and multivariate logistic regression analysis on multiple parameters including age, gender, smoking status, neck circumference, waist circumference, BMI, stage and histopathology of lung cancer, presence of superior vena cava obstruction, and performance status to find out the factors that are independently associated with a diagnosis of moderate-to-severe OSA. RESULTS: Our results suggest that poor performance status is the most significant predictor of moderate to severe OSA in patients with lung cancer after controlling for important confounders. CONCLUSION: Performance status is a predictor of moderate to severe OSA in patients with lung cancer in our population of middle eastern ethnicity.

Treating sleep-disordered breathing of idiopathic pulmonary fibrosis patients with CPAP and nocturnal oxygen treatment. A pilot study : Sleep-disordered breathing treatment in IPF.

INTRODUCTION: Sleep-disordered breathing (SDB) is a major comorbidity in idiopathic pulmonary fibrosis (IPF) and is associated with a poor outcome. There is a lack of knowledge regarding the impact of SDB treatment on IPF. We assessed at one year: (1) the effect of CPAP and/or nocturnal oxygen therapy on IPF regarding lung function, blood mediators, and quality of life; (2) adherence to SDB treatment and SDB changes. METHODOLOGY: This is a prospective study of consecutive newly diagnosed IPF patients initiating anti-fibrotic treatment. Lung function, polysomnography, blood tests and quality of life questionnaires were performed at inclusion and after one year. Patients were classified as obstructive sleep apnoea (OSA), central sleep apnoea (CSA), and sleep-sustained hypoxemia (SSH). SDB therapy (CPAP and/or nocturnal oxygen therapy) was initiated if needed. RESULTS: Fifty patients were enrolled (36% had OSA, 22% CSA, and 12% SSH). CPAP was started in 54% of patients and nocturnal oxygen therapy in 16%. At one-year, polysomnography found improved parameters, though 17% of patients had to add nocturnal oxygen therapy or CPAP, while 33% presented SDB onset at this second polysomnography. CPAP compliance at one year was 6.74 h/night (SD 0.74). After one year, matrix metalloproteinase-1 decreased in OSA and CSA (p = 0.029; p = 0.027), C-reactive protein in OSA (p = 0.045), and surfactant protein D in CSA group (p = 0.074). There was no significant change in lung function. CONCLUSIONS: Treatment of SBD with CPAP and NOT can be well tolerated with a high compliance. IPF patients may exhibit SDB progression and require periodic re-assessment. Further studies to evaluate the impact of SDB treatment on lung function and serological mediators are needed.

Self-applied somnography: technical feasibility of electroencephalography and electro-oculography signal characteristics in sleep staging of suspected sleep-disordered adults.

Sleep recordings are increasingly being conducted in patients' homes where patients apply the sensors themselves according to instructions. However, certain sensor types such as cup electrodes used in conventional polysomnography are unfeasible for self-application. To overcome this, self-applied forehead montages with electroencephalography and electro-oculography sensors have been developed. We evaluated the technical feasibility of a self-applied electrode set from Nox Medical (Reykjavik, Iceland) through home sleep recordings of healthy and suspected sleep-disordered adults (n = 174) in the context of sleep staging. Subjects slept with a double setup of conventional type II polysomnography sensors and self-applied forehead sensors. We found that the self-applied electroencephalography and electro-oculography electrodes had acceptable impedance levels but were more prone to losing proper skin-electrode contact than the conventional cup electrodes. Moreover, the forehead electroencephalography signals recorded using the self-applied electrodes expressed lower amplitudes (difference 25.3%-43.9%, p < 0.001) and less absolute power (at 1-40 Hz, p < 0.001) than the polysomnography electroencephalography signals in all sleep stages. However, the signals recorded with the self-applied electroencephalography electrodes expressed more relative power (p < 0.001) at very low frequencies (0.3-1.0 Hz) in all sleep stages. The electro-oculography signals recorded with the self-applied electrodes expressed comparable characteristics with standard electro-oculography. In conclusion, the results support the technical feasibility of the self-applied electroencephalography and electro-oculography for sleep staging in home sleep recordings, after adjustment for amplitude differences, especially for scoring Stage N3 sleep.

First-night effect in insomnia disorder: a systematic review and meta-analysis of polysomnographic findings.

Polysomnographic studies have been performed to investigate the first-night effect in insomnia disorder. However, these studies have revealed discrepant findings. This meta-analysis aimed to summarise and quantify the characteristics of the first-night effect in insomnia disorder. We performed a systematic search of the PubMed, Medline, EMBASE, Web of Science and PsycINFO databases to identify studies published through October 2019. A total of 11,862 articles were identified, and seven studies with eight independent populations were included in the meta-analysis. A total of 639 patients with insomnia disorder and 171 healthy controls underwent more than 2 consecutive nights of in-laboratory polysomnography. Pooled results demonstrated that both variables of sleep continuity and sleep architecture, other than slow-wave sleep were significantly altered in the first-night effect in insomnia disorder. Furthermore, the results indicated that patients with insomnia disorder had a disruption of sleep continuity in the first-night effect, including increased sleep onset latency and reduced total sleep time, compared to healthy controls. Overall, the findings show that patients with insomnia disorder experience the first-night effect, rather than reverse first-night effect, and the profiles of the first-night effect in patients with insomnia are different from healthy controls. These indicate that an adaptation night is necessary when sleep continuity and sleep architecture is to be studied in patients with insomnia disorder. More well-designed studies with large samples are needed to confirm the results.

A novel deep learning model based on transformer and cross modality attention for classification of sleep stages.

The classification of sleep stages is crucial for gaining insights into an individual's sleep patterns and identifying potential health issues. Employing several important physiological channels in different views, each providing a distinct perspective on sleep patterns, can have a great impact on the efficiency of the classification models. In the context of neural networks and deep learning models, transformers are very effective, especially when dealing with time series data, and have shown remarkable compatibility with sequential data analysis as physiological channels. On the other hand, cross-modality attention by integrating information from multiple views of the data enables to capture relationships among different modalities, allowing models to selectively focus on relevant information from each modality. In this paper, we introduce a novel deep-learning model based on transformer encoder-decoder and cross-modal attention for sleep stage classification. The proposed model processes information from various physiological channels with different modalities using the Sleep Heart Health Study Dataset (SHHS) data and leverages transformer encoders for feature extraction and cross-modal attention for effective integration to feed into the transformer decoder. The combination of these elements increased the accuracy of the model up to 91.33% in classifying five classes of sleep stages. Empirical evaluations demonstrated the model's superior performance compared to standalone approaches and other state-of-the-art techniques, showcasing the potential of combining transformer and cross-modal attention for improved sleep stage classification.

Associations between body mass index, sleep-disordered breathing, brain structure, and behavior in healthy children.

Pediatric overweight/obesity can lead to sleep-disordered breathing (SDB), abnormal neurological and cognitive development, and psychiatric problems, but the associations and interactions between these factors have not been fully explored. Therefore, we investigated the associations between body mass index (BMI), SDB, psychiatric and cognitive measures, and brain morphometry in 8484 children 9-11 years old using the Adolescent Brain Cognitive Development dataset. BMI was positively associated with SDB, and both were negatively correlated with cortical thickness in lingual gyrus and lateral orbitofrontal cortex, and cortical volumes in postcentral gyrus, precentral gyrus, precuneus, superior parietal lobule, and insula. Mediation analysis showed that SDB partially mediated the effect of overweight/obesity on these brain regions. Dimensional psychopathology (including aggressive behavior and externalizing problem) and cognitive function were correlated with BMI and SDB. SDB and cortical volumes in precentral gyrus and insula mediated the correlations between BMI and externalizing problem and matrix reasoning ability. Comparisons by sex showed that obesity and SDB had a greater impact on brain measures, cognitive function, and mental health in girls than in boys. These findings suggest that preventing childhood obesity will help decrease SDB symptom burden, abnormal neurological and cognitive development, and psychiatric problems.

Automatic quantification of REM sleep without atonia reliably identifies patients with REM sleep behavior disorder: a possible screening tool?

BACKGROUND: REM Sleep Behavior Disorder (RBD) is characterized by absence of physiological muscle atonia during REM sleep (REM sleep without atonia, RWA). Nigro-striatal dopaminergic impairment is a feature of Parkinson disease (PD) and can be identified in prodromal stages as well, such as idiopathic RBD (iRBD). Aims of this study are to explore the efficacy of an automatic RWA quantification in identifying RBD patients and the correlation between RWA and nigro-striatal dopaminergic function. METHODS: Forty-five iRBD, 46 PD with RBD, 24 PD without RBD patients and 11 healthy controls were enrolled in the Genoa Center (group A) and 25 patients with iRBD (group B) were enrolled in the Danish Center. Group A underwent brain [123I]FP-CIT-SPECT and group B underwent brain [18F]PE2I-PET as measures of nigro-striatal dopaminergic function. Chin muscle activity was recorded in all subjects and analyzed by applying a published automatic algorithm. Correlations between RWA and nigro-striatal dopaminergic function were explored. RESULTS: The automatic quantification of RWA significantly differentiated RBD from non-RBD subjects (AUC = 0.86), although with lower accuracy compared with conventional visual scoring (AUC = 0.99). No significant correlation was found between RWA and nigro-striatal dopaminergic function. CONCLUSION: The automatic quantification of RWA is a reliable tool to identify subjects with RBD and may be used as a first-line screening tool, but without correlations with nigro-striatal dopaminergic functioning.

How to objectively measure snoring: a systematic review.

PURPOSE: Snoring is the most common symptom of obstructive sleep apnea. Various objective methods of measuring snoring are available, and even if the measurement is performed the same way, communication is difficult because there are no common reference values between the researcher and clinician with regard to intensity and frequency, among other variables. In other words, no consensus regarding objective measurement has been reached. This study aimed to review the literature related to the objective measurement of snoring, such as measurement devices, definitions, and device locations. METHODS: A literature search based on the PubMed, Cochrane, and Embase databases was conducted from the date of inception to April 5, 2023. Twenty-nine articles were included in this study. Articles that mentioned only the equipment used for measurement and did not include individual details were excluded from the study. RESULTS: Three representative methods for measuring snoring emerged. These include (1) a microphone, which measures snoring sound; (2) piezoelectric sensor, which measures snoring vibration; and (3) nasal transducer, which measures airflow. In addition, recent attempts have been made to measure snoring using smartphones and applications. CONCLUSION: Numerous studies have investigated both obstructive sleep apnea and snoring. However, the objective methods of measuring snoring and snoring-related concepts vary across studies. Consensus in the academic and clinical communities on how to measure and define snoring is required.

Does oral appliance efficacy in treating obstructive sleep apnea depend on the appliance specialist's experience?

PURPOSE: In France, oral appliances (OAs) are the first-line treatment for moderate and second-line treatment for severe obstructive sleep apnea-hypopnea syndrome. In general, the sleep specialist refers his/her patient to the appliance specialist for the impressions and the fitting. However, is there a relationship between the volume of activity of the appliance specialist and the efficacy of this device? METHODS: Our unit includes seven appliance otolaryngology specialists whose activities are highly variable (number of patients varying by a factor of almost 10). Data from a prospective follow-up registry of patients treated with an OA for moderate and severe obstructive sleep apnea-hypopnea syndrome were studied, and differences in outcomes between practitioners in the team were sought. RESULTS: Among 859 patients, OAs significantly reduced the apnea-hypopnea index. Even if the patients were not completely comparable from one practitioner to another, there was a significant heterogeneity in efficacy (complete or partial response of the apnea-hypopnea index and failure of OAs) between practitioners (p = 0.0038; 0.0011; 0.0007 respectively), with better results in practitioners with a higher level of OA activity. CONCLUSION: The findings suggest that it may be preferable to refer patients to OA practitioners who see a higher volume of patients with moderate or severe obstructive sleep apnea-hypopnea syndrome treated with an OA.

Preliminary assessment of portable sleep monitoring for diagnosis of obstructive sleep apnea in children.

OBJECTIVE: By observing the differences in sleep parameters between portable sleep monitoring (PM) and polysomnography (PSG) in children, we aimed to investigate the diagnostic value and feasibility of PM in children with suspected obstructive sleep apnea (OSA). STUDY DESIGN: This prospective study enrolled consecutive children (aged 3-14 years) with suspected OSA in Shenzhen Children's Hospital. They had PSG and PM in the sleep laboratory. Clinical parameters of the two sleep monitoring methods were compared. RESULTS: A total of 58 children participated. They were classified into two groups according to age: 28 children aged 3 to 5 years and 30 children aged 6 to 14 years. No significant differences were observed in apnea-hypopnea index (AHI), lowest oxygen saturation (LSaO2), and mean oxygen saturation (MSaO2) between PM and PSG, but the sleep efficiency with PM was significantly higher (3-5 years age: 92.2 ± 11.3% vs 85.2 ± 14.3%, 6-14 years age: 93.2 ± 14.5% vs 84.8 ± 16.3%, both P < 0.05) than the sleep efficiency with PSG. Pearson correlation analysis indicated a strong correlation between AHI, LSaO2, MSaO2, and sleep efficiency measured by PSG and PM. Receiver operating characteristic curve (ROC) analysis showed that PM was a reliable diagnostic tool for OSA. PM has high sensitivity (3-5 years age: 95.8%, 6-14 years age: 96.3%) and low specificity (3-5 years age: 25.0%, 6-14 years age: 33.3%) for OSA in children. Thus, there is a low rate of missed diagnoses, but there is some inaccuracy in excluding children who do not have OSA. CONCLUSION: The results showed that PM has a good correlation with the various parameters of PSG. PM may be a reliable tool for diagnosing moderate and severe OSA in children, especially those who cannot cooperate with PSG or who have limited access to PSG.

Polysomnographic findings in children with 22q deletion & duplication syndrome: relationship to genetic diagnosis, parent-reported symptoms, and calcium levels.

PURPOSE: 22q11.2 deletion syndrome (22q11.2DS) is the most common microdeletion syndrome. In the current study, we assessed the relationship between parent-reported symptoms of obstructive sleep apnea (OSA) and polysomnographic (PSG) results in patients with 22q11.2DS. Additionally, we explored the relationships between genetic diagnosis, serum calcium and ferritin levels, and PSG results. METHODS: Retrospective chart review was completed for patients enrolled in our 22q Center's registry from 2015-2021. Data extracted included: patient characteristics, parent-reported sleep symptoms from the Childhood Sleep Habits Questionnaire (CSHQ), serum calcium and ferritin levels, and results from formal PSG. RESULTS: Overall, n = 89 encounters (60 unique patients) with PSG data demonstrated that there were no differences in OSA between those with deletion vs duplication, but PLMD was more common in those with deletion (35% vs 7%, p = 0.032). In a subset of n = 24 encounters with PSG and survey data in proximity, there were no significant associations between the CSHQ sleep-disordered breathing subscale and OSA presence or severity (p = 0.842). Likewise, we found no significant associations between the individual symptoms of OSA and PSG results (all p > 0.5). In those patients with available calcium (n = 44) and ferritin (n = 17) levels, we found a significant negative correlation between serum calcium and PLMS (r = -0.446, p = 0.002), but not ferritin (r = -0.067, p = 0.797) levels. CONCLUSIONS: Parent-reported symptoms do not predict the presence or severity of OSA in children with 22q11.2DS. There was a negative correlation between serum calcium, but not ferritin, and PLMS on PSG.

Age-related changes in sleep spindle characteristics in individuals over 75 years of age: a retrospective and comparative study.

BACKGROUND: Sleep and its architecture are affected and changing through the whole lifespan. We know main modifications of the macro-architecture with a shorter sleep, occurring earlier and being more fragmented. We have been studying sleep micro-architecture through its pathological modification in sleep, psychiatric or neurocognitive disorders whereas we are still unable to say if the sleep micro-architecture of an old and very old person is rather normal, under physiological changes, or a concern for a future disorder to appear. We wanted to evaluate age-related changes in sleep spindle characteristics in individuals over 75 years of age compared with younger individuals. METHODS: This was an exploratory study based on retrospective and comparative laboratory-based polysomnography data registered in the normal care routine for people over 75 years of age compared to people aged 65-74 years. We were studying their sleep spindle characteristics (localization, density, frequency, amplitude, and duration) in the N2 and N3 sleep stages. ANOVA and ANCOVA using age, sex and OSA were applied. RESULTS: We included 36 participants aged > 75 years and 57 participants aged between 65 and 74 years. An OSA diagnosis was most common in both groups. Older adults receive more medication to modify their sleep. Spindle localization becomes more central after 75 years of age. Changes in the other sleep spindle characteristics between the N2 and N3 sleep stages and between the slow and fast spindles were conformed to literature data, but age was a relevant modifier only for density and duration. CONCLUSION: We observed the same sleep spindle characteristics in both age groups except for localization. We built our study on a short sample, and participants were not free of all sleep disorders. We could establish normative values through further studies with larger samples of people without any sleep disorders to understand the modifications in normal aging and pathological conditions and to reveal the predictive biomarker function of sleep spindles.

Detection of Sleep Apnea Using Wearable AI: Systematic Review and Meta-Analysis.

BACKGROUND: Early detection of sleep apnea, the health condition where airflow either ceases or decreases episodically during sleep, is crucial to initiate timely interventions and avoid complications. Wearable artificial intelligence (AI), the integration of AI algorithms into wearable devices to collect and analyze data to offer various functionalities and insights, can efficiently detect sleep apnea due to its convenience, accessibility, affordability, objectivity, and real-time monitoring capabilities, thereby addressing the limitations of traditional approaches such as polysomnography. OBJECTIVE: The objective of this systematic review was to examine the effectiveness of wearable AI in detecting sleep apnea, its type, and its severity. METHODS: Our search was conducted in 6 electronic databases. This review included English research articles evaluating wearable AI's performance in identifying sleep apnea, distinguishing its type, and gauging its severity. Two researchers independently conducted study selection, extracted data, and assessed the risk of bias using an adapted Quality Assessment of Studies of Diagnostic Accuracy-Revised tool. We used both narrative and statistical techniques for evidence synthesis. RESULTS: Among 615 studies, 38 (6.2%) met the eligibility criteria for this review. The pooled mean accuracy, sensitivity, and specificity of wearable AI in detecting apnea events in respiration (apnea and nonapnea events) were 0.893, 0.793, and 0.947, respectively. The pooled mean accuracy of wearable AI in differentiating types of apnea events in respiration (normal, obstructive sleep apnea, central sleep apnea, mixed apnea, and hypopnea) was 0.815. The pooled mean accuracy, sensitivity, and specificity of wearable AI in detecting sleep apnea were 0.869, 0.938, and 0.752, respectively. The pooled mean accuracy of wearable AI in identifying the severity level of sleep apnea (normal, mild, moderate, and severe) and estimating the severity score (Apnea-Hypopnea Index) was 0.651 and 0.877, respectively. Subgroup analyses found different moderators of wearable AI performance for different outcomes, such as the type of algorithm, type of data, type of sleep apnea, and placement of wearable devices. CONCLUSIONS: Wearable AI shows potential in identifying and classifying sleep apnea, but its current performance is suboptimal for routine clinical use. We recommend concurrent use with traditional assessments until improved evidence supports its reliability. Certified commercial wearables are needed for effectively detecting sleep apnea, predicting its occurrence, and delivering proactive interventions. Researchers should conduct further studies on detecting central sleep apnea, prioritize deep learning algorithms, incorporate self-reported and nonwearable data, evaluate performance across different device placements, and provide detailed findings for effective meta-analyses.

Obstructive Sleep Apnea in Adults: Common Questions and Answers.

Obstructive sleep apnea (OSA) is a common disorder that affects quality of life and is associated with comorbidities such as hypertension, atrial fibrillation, heart failure, coronary heart disease, type 2 diabetes mellitus, and stroke. OSA is characterized by a reduction or cessation of breathing during sleep, resulting in intermittent hypoxemia, autonomic fluctuation, and sleep fragmentation. The U.S. Preventive Services Task Force states that there is insufficient evidence to recommend routine screening for OSA in the absence of symptoms. OSA should be considered in patients with excessive daytime fatigue, unrestful sleep, persistent snoring, and nocturnal awakenings with gasping or choking. The STOP-BANG questionnaire is the most sensitive screening tool for OSA, and the diagnostic standard is polysomnography with an observed apnea-hypopnea index greater than 5 in the presence of symptoms or greater than 15 without symptoms. Home sleep apnea testing is a useful diagnostic option in patients who have symptoms consistent with moderate to severe OSA without significant cardiopulmonary comorbidities. Positive airway pressure, with a humidified nasal or facial mask, is the first-line treatment for adults with OSA. Weight loss is a beneficial adjunct to treatment through intensive lifestyle modification, medications, or bariatric surgery. Alternatives for patients intolerant of or nonadherent to positive airway pressure include changing the type of mask used, mandibular advancement devices, hypoglossal nerve stimulation, and other surgical interventions. Although many OSA therapies effectively improve daytime sleepiness and blood pressure, none have demonstrated a mortality benefit in randomized controlled trials.

Wavelet analysis of intermittent dynamics in nocturnal electrocardiography and electroencephalography data.

This paper presents the results of a study of the characteristics of phase synchronization between electrocardiography(ECG) and electroencephalography (EEG) signals during night sleep. Polysomnographic recordings of eight generally healthy subjects and eight patients with obstructive sleep apnea syndrome were selected as experimental data. A feature of this study was the introduction of an instantaneous phase for EEG and ECG signals using a continuous wavelet transform at the heart rate frequency using the concept of time scale synchronization, which eliminated the emergence of asynchronous areas of behavior associated with the "leaving" of the fundamental frequency of the cardiovascular system. Instantaneous phase differences were examined for various pairs of EEG and ECG signals during night sleep, and it was shown that in all cases the phase difference exhibited intermittency. Laminar areas of behavior are intervals of phase synchronization, i.e., phase capture. Turbulent intervals are phase jumps of 2π. Statistical studies of the observed intermittent behavior were carried out, namely, distributions of the duration of laminar sections of behavior were estimated. For all pairs of channels, the duration of laminar phases obeyed an exponential law. Based on the analysis of the movement of the phase trajectory on a rotating plane at the moment of detection of the turbulent phase, it was established that in this case the eyelet intermittency was observed. There was no connection between the statistical characteristics of laminar phase distributions for intermittent behavior and the characteristics of night breathing disorders (apnea syndrome). It was found that changes in statistical characteristics in the phase synchronization of EEG and ECG signals were correlated with blood pressure at the time of signal recording in the subjects, which is an interesting effect that requires further research.

Nap polysomnography in infants with laryngomalacia as a tool to predict treatment strategy.

PURPOSE: This study aimed to investigate the role of nap polysomnography (NPSG) in predicting treatment strategies for infants with moderate to severe laryngomalacia and to explore the association between obstructive sleep apnea (OSA) severity, weight gain, and laryngomalacia severity. METHODS: A retrospective analysis was conducted on infants diagnosed with moderate to severe laryngomalacia who underwent NPSG between January 2019 and June 2023. Clinical variables, NPSG parameters, and treatment decisions were collected. Weight gain rate and its correlation with NPSG indices were assessed. Logistic regression analyses were performed to predict treatment strategies based on NPSG findings. RESULTS: Of the 39 infants included (median age: 3.3 months), 77% exhibited OSA, with 69% having moderate to severe OSA [apnea-hypopnea index (AHI) > 5/h]. Weight gain rate correlated negatively with indices of OSA severity, including the hypopnea index (HI) and the AHI. In a multiple logistic regression analysis incorporating the severity of OSA (AHI), weight gain rate, and laryngomalacia severity, only AHI predicted the decision for surgical or non-invasive ventilation treatment (OR = 2.1, CI95 [1.6; 2.8], p ≤ 10-4). The weight gain rate was predicted (r2 = 0.28) by the AHI and the presence of retractions of auxiliary inspiratory muscles. CONCLUSION: This study underscores the importance of NPSG in assessing infants with moderate to severe laryngomalacia. The AHI from NPSG emerged as a potential predictor for treatment decisions and weight gain rate, emphasizing its clinical relevance. These findings advocate incorporating NPSG into the diagnostic and management process for infants with laryngomalacia.

Evaluating Accuracy in Five Commercial Sleep-Tracking Devices Compared to Research-Grade Actigraphy and Polysomnography.

The development of consumer sleep-tracking technologies has outpaced the scientific evaluation of their accuracy. In this study, five consumer sleep-tracking devices, research-grade actigraphy, and polysomnography were used simultaneously to monitor the overnight sleep of fifty-three young adults in the lab for one night. Biases and limits of agreement were assessed to determine how sleep stage estimates for each device and research-grade actigraphy differed from polysomnography-derived measures. Every device, except the Garmin Vivosmart, was able to estimate total sleep time comparably to research-grade actigraphy. All devices overestimated nights with shorter wake times and underestimated nights with longer wake times. For light sleep, absolute bias was low for the Fitbit Inspire and Fitbit Versa. The Withings Mat and Garmin Vivosmart overestimated shorter light sleep and underestimated longer light sleep. The Oura Ring underestimated light sleep of any duration. For deep sleep, bias was low for the Withings Mat and Garmin Vivosmart while other devices overestimated shorter and underestimated longer times. For REM sleep, bias was low for all devices. Taken together, these results suggest that proportional bias patterns in consumer sleep-tracking technologies are prevalent and could have important implications for their overall accuracy.